Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes

Abstract

This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy 1) has zero distributional (average) effect for all subpopulations defined by covariate values, and 2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan-Meier integrals, and do not rely on parametric distributional assumptions, shape restrictions, nor on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation, but can also account for treatment noncompliance, an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric η^-1/2 -rate, η being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study, and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.

Keywords: Causal inference, Duration data, Empirical Process, Kaplan-Meier, Survival Analysis

JEL Classification: C01, C12, C14, C21, C41, J64